Plots

knitr::opts_chunk$set(echo = TRUE)

library(tidyverse)
library(here)

path <- "../output/current/"

allfiles <- list.files(path, ".csv")

for(f in allfiles) {
  fname <- paste0(path,f)
  dat <- read.csv(fname) %>%
    filter(variable %in% c("actual_daily_cases")) %>%
    mutate(date = as.Date(date))

  read.csv(fname) %>%
    filter(variable %in% c("daily_cases")) %>%
    filter(sim_type == "status_quo") %>%
    mutate(date = as.Date(date)) %>%
    filter(date <= (Sys.Date() + 7*4)) %>%
    ggplot(aes(x = date, y = median_value)) +
    geom_ribbon(aes(ymin = lower_80, ymax = upper_80), alpha = 0.2) +
    geom_line() +
    geom_line(data = dat, aes(x = date, y = mean_value), color = "blue") +
    ylab("daily cases") +
    ggtitle(f) -> out
  print(out)

  dat <- read.csv(fname) %>%
    filter(variable %in% c("actual_daily_deaths")) %>%
    mutate(date = as.Date(date))
  read.csv(fname) %>%
    filter(variable %in% c("daily_deaths")) %>%
    filter(sim_type == "status_quo") %>%
    mutate(date = as.Date(date)) %>%
    filter(date <= (Sys.Date() + 7*4)) %>%
    ggplot(aes(x = date, y = median_value)) +
    geom_ribbon(aes(ymin = lower_80, ymax = upper_80), alpha = 0.2) +
    geom_line() +
    ylab("daily deaths") +
    geom_line(data = dat, aes(x = date, y = mean_value), color = "blue") +
    ggtitle(f) -> out3
  print(out3)
  
  read.csv(fname) %>%
    filter(variable %in% c("combined_trend", "latent_trend", "mobility_trend")) %>%
    filter(sim_type == "status_quo") %>%
    mutate(date = as.Date(date)) %>%
    ggplot(aes(x = date, y = mean_value)) +
    geom_point(aes(color = variable)) +
    ggtitle(f) -> out2
  print(out2)
}

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